{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# k-Nearest Neighbor (kNN) exercise\n",
    "\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "The kNN classifier consists of two stages:\n",
    "\n",
    "- During training, the classifier takes the training data and simply remembers it\n",
    "- During testing, kNN classifies every test image by comparing to all training images and transfering the labels of the k most similar training examples\n",
    "- The value of k is cross-validated\n",
    "\n",
    "In this exercise you will implement these steps and understand the basic Image Classification pipeline, cross-validation, and gain proficiency in writing efficient, vectorized code.\n",
    "\n",
    "完成并连同作业缴交此完整的工作表(包括其输出及工作表外的任何支援程式码)。更多详情请参见课程网站上的[作业页]。*\n",
    "\n",
    "kNN分类器分为两个阶段:\n",
    "\n",
    "-训练时，分类器获取训练数据并简单记忆\n",
    "-在测试过程中，kNN将每个测试图像与所有的训练图像进行比较，并将k个最相似的训练实例的标签进行转移，从而对每个测试图像进行分类\n",
    "- k的值是交叉验证的\n",
    "\n",
    "在本练习中，您将实现这些步骤并了解基本的图像分类管道、交叉验证，并熟练地编写高效的向量化代码。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "code_folding": [],
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "# Run some setup code for this notebook.\n",
    "\n",
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# This is a bit of magic to make matplotlib figures appear inline in the notebook\n",
    "# rather than in a new window.\n",
    "# 使matplotlib图形内联出现在笔记本中，而不是出现在新窗口中，这有点神奇。\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# Some more magic so that the notebook will reload external python modules;\n",
    "# 一些更神奇的，使笔记本将重新加载外部python模块;\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "# %reload_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (50000, 32, 32, 3)\n",
      "Training labels shape:  (50000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the raw CIFAR-10 data.\n",
    "# cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "cifar10_dir = 'E:\\cifar-10-batches-py'\n",
    "\n",
    "# Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n",
    "# 清除变量以防止多次加载数据(这可能会导致内存问题)\n",
    "try:\n",
    "   del X_train, y_train\n",
    "   del X_test, y_test\n",
    "   print('Clear previously loaded data.')\n",
    "except:\n",
    "   pass\n",
    "\n",
    "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "# As a sanity check, we print out the size of the training and test data.\n",
    "# 作为一个完整性检查，我们打印出训练和测试数据的大小。\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Training labels shape: ', y_train.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 70 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize some examples from the dataset.\n",
    "# We show a few examples of training images from each class.\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "samples_per_class = 7\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_train == y)\n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)\n",
    "        plt.imshow(X_train[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 3072) (500, 3072)\n"
     ]
    }
   ],
   "source": [
    "# Subsample the data for more efficient code execution in this exercise\n",
    "# 在本练习中，为了更有效地执行代码，对数据进行了细分\n",
    "num_training = 5000\n",
    "mask = list(range(num_training))\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "num_test = 500\n",
    "mask = list(range(num_test))\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]\n",
    "\n",
    "# Reshape the image data into rows\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "print(X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "from cs231n.classifiers import KNearestNeighbor\n",
    "\n",
    "# Create a kNN classifier instance. \n",
    "# Remember that training a kNN classifier is a noop: \n",
    "# the Classifier simply remembers the data and does no further processing \n",
    "# 创建一个kNN分类器实例。\n",
    "# 记住，训练一个kNN分类器是一个noop:\n",
    "# 分类器只记住数据，不做进一步的处理\n",
    "classifier = KNearestNeighbor()\n",
    "classifier.train(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We would now like to classify the test data with the kNN classifier. Recall that we can break down this process into two steps: \n",
    "\n",
    "1. First we must compute the distances between all test examples and all train examples. \n",
    "2. Given these distances, for each test example we find the k nearest examples and have them vote for the label\n",
    "\n",
    "Lets begin with computing the distance matrix between all training and test examples. For example, if there are **Ntr** training examples and **Nte** test examples, this stage should result in a **Nte x Ntr** matrix where each element (i,j) is the distance between the i-th test and j-th train example.\n",
    "\n",
    "**Note: For the three distance computations that we require you to implement in this notebook, you may not use the np.linalg.norm() function that numpy provides.**\n",
    "\n",
    "First, open `cs231n/classifiers/k_nearest_neighbor.py` and implement the function `compute_distances_two_loops` that uses a (very inefficient) double loop over all pairs of (test, train) examples and computes the distance matrix one element at a time.\n",
    "\n",
    "\n",
    "我们现在想用kNN分类器对测试数据进行分类。我们可以把这个过程分成两个步骤:\n",
    "\n",
    "首先，我们必须计算所有测试样例和所有训练样例之间的距离。\n",
    "\n",
    "给定这些距离，对于每个测试示例，我们找到k个最近的示例，并让它们为标签投票\n",
    "\n",
    "让我们从计算所有训练和测试示例之间的距离矩阵开始。\n",
    "\n",
    "例如，如果有Ntr训练示例和Nte测试示例，则此阶段应该生成Nte x Ntr矩阵，其中每个元素(i,j)是第i个测试和第j个训练示例之间的距离。\n",
    "\n",
    "注意:对于我们要求您在本笔记本中实现的三个距离计算，您可能不使用numpy提供的np.linalg.norm()函数。\n",
    "\n",
    "首先，打开`cs231n/classifiers/k_nearest_neighbor.py` 并实现`compute_distances_two_loops`函数，该函数在所有对(测试、培训)示例上使用一个(非常低效的)双循环，并一次计算一个元素的距离矩阵。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 5000)\n"
     ]
    }
   ],
   "source": [
    "# Open cs231n/classifiers/k_nearest_neighbor.py and implement\n",
    "# compute_distances_two_loops.\n",
    "\n",
    "# Test your implementation:\n",
    "dists = classifier.compute_distances_two_loops(X_test)\n",
    "print(dists.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can visualize the distance matrix: each row is a single test example and\n",
    "# its distances to training examples\n",
    "# 我们可以可视化距离矩阵：每一行都是一个测试示例 和 它与训练示例的距离\n",
    "plt.imshow(dists, interpolation='none')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline Question 1** \n",
    "\n",
    "Notice the structured patterns in the distance matrix, where some rows or columns are visible brighter. \n",
    "\n",
    "(Note that with the default color scheme black indicates low distances while white indicates high distances.)\n",
    "\n",
    "- What in the data is the cause behind the distinctly bright rows?\n",
    "- What causes the columns?\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$ *fill this in.*\n",
    "\n",
    "注意距离矩阵中的结构化模式，其中一些行或列更亮。\n",
    "\n",
    "(注意，在默认的配色方案中，黑色表示低距离，白色表示高距离。)\n",
    "\n",
    "-数据中那些明显明亮的行背后的原因是什么?\n",
    "-是什么导致了这些列?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 137 / 500 correct => accuracy: 0.274000\n"
     ]
    }
   ],
   "source": [
    "# Now implement the function predict_labels and run the code below:\n",
    "# We use k = 1 (which is Nearest Neighbor).\n",
    "y_test_pred = classifier.predict_labels(dists, k=1)\n",
    "\n",
    "# Compute and print the fraction of correctly predicted examples\n",
    "# 计算并打印正确预测的示例的分数\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should expect to see approximately `27%` accuracy. \n",
    "\n",
    "Now lets try out a larger `k`, say `k = 5`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 139 / 500 correct => accuracy: 0.278000\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = classifier.predict_labels(dists, k=5)\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should expect to see a slightly better performance than with `k = 1`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline Question 2**\n",
    "\n",
    "We can also use other distance metrics such as L1 distance.\n",
    "For pixel values $p_{ij}^{(k)}$ at location $(i,j)$ of some image $I_k$, \n",
    "\n",
    "the mean $\\mu$ across all pixels over all images is $$\\mu=\\frac{1}{nhw}\\sum_{k=1}^n\\sum_{i=1}^{h}\\sum_{j=1}^{w}p_{ij}^{(k)}$$\n",
    "And the pixel-wise mean $\\mu_{ij}$ across all images is \n",
    "$$\\mu_{ij}=\\frac{1}{n}\\sum_{k=1}^np_{ij}^{(k)}.$$\n",
    "The general standard deviation $\\sigma$ and pixel-wise standard deviation $\\sigma_{ij}$ is defined similarly.\n",
    "\n",
    "Which of the following preprocessing steps will not change the performance of a Nearest Neighbor classifier that uses L1 distance? Select all that apply.\n",
    "1. Subtracting the mean $\\mu$ ($\\tilde{p}_{ij}^{(k)}=p_{ij}^{(k)}-\\mu$.)\n",
    "2. Subtracting the per pixel mean $\\mu_{ij}$  ($\\tilde{p}_{ij}^{(k)}=p_{ij}^{(k)}-\\mu_{ij}$.)\n",
    "3. Subtracting the mean $\\mu$ and dividing by the standard deviation $\\sigma$.\n",
    "4. Subtracting the pixel-wise mean $\\mu_{ij}$ and dividing by the pixel-wise standard deviation $\\sigma_{ij}$.\n",
    "5. Rotating the coordinate axes of the data.\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$\n",
    "\n",
    "\n",
    "$\\color{blue}{\\textit Your Explanation:}$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "One loop difference was: 0.000000\n",
      "Good! The distance matrices are the same\n"
     ]
    }
   ],
   "source": [
    "# Now lets speed up distance matrix computation by using partial vectorization\n",
    "# with one loop. \n",
    "# 现在，通过使用一个循环的部分矢量化来加快距离矩阵的计算。\n",
    "# Implement the function compute_distances_one_loop and run the code below:\n",
    "dists_one = classifier.compute_distances_one_loop(X_test)\n",
    "\n",
    "# To ensure that our vectorized implementation is correct, we make sure that it\n",
    "# agrees with the naive implementation. There are many ways to decide whether\n",
    "# two matrices are similar; one of the simplest is the Frobenius norm. In case\n",
    "# you haven't seen it before, the Frobenius norm of two matrices is the square\n",
    "# root of the squared sum of differences of all elements; in other words, reshape\n",
    "# the matrices into vectors and compute the Euclidean distance between them.\n",
    "# 为了确保向量化的实现是正确的，我们要确保它与初始实现是一致的。\n",
    "# 判断两个矩阵是否相似的方法有很多种;其中最简单的是Frobenius范数。\n",
    "# 如果你以前没见过，两个矩阵的Frobenius范数是所有元素的差的平方和的平方根;\n",
    "# 换句话说，将矩阵重新组合成向量并计算它们之间的欧氏距离。\n",
    "\n",
    "difference = np.linalg.norm(dists - dists_one, ord='fro')\n",
    "print('One loop difference was: %f' % (difference, ))\n",
    "if difference < 0.001:\n",
    "    print('Good! The distance matrices are the same')\n",
    "else:\n",
    "    print('Uh-oh! The distance matrices are different')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "code_folding": [],
    "scrolled": true,
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No loop difference was: 0.000000\n",
      "Good! The distance matrices are the same\n"
     ]
    }
   ],
   "source": [
    "# Now implement the fully vectorized version inside compute_distances_no_loops\n",
    "# and run the code\n",
    "# 现在在compute_distances_no_loops内部实现完全矢量化的版本并运行代码\n",
    "dists_two = classifier.compute_distances_no_loops(X_test)\n",
    "\n",
    "# check that the distance matrix agrees with the one we computed before:\n",
    "# 检查距离矩阵是否与我们之前计算出的矩阵一致：\n",
    "difference = np.linalg.norm(dists - dists_two, ord='fro')\n",
    "print('No loop difference was: %f' % (difference, ))\n",
    "if difference < 0.001:\n",
    "    print('Good! The distance matrices are the same')\n",
    "else:\n",
    "    print('Uh-oh! The distance matrices are different')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Two loop version took 45.506625 seconds\n",
      "One loop version took 82.338715 seconds\n",
      "No loop version took 0.427333 seconds\n"
     ]
    }
   ],
   "source": [
    "# Let's compare how fast the implementations are\n",
    "def time_function(f, *args):\n",
    "    \"\"\"\n",
    "    Call a function f with args and return the time (in seconds) that it took to execute.\n",
    "    \"\"\"\n",
    "    import time\n",
    "    tic = time.time()\n",
    "    f(*args)\n",
    "    toc = time.time()\n",
    "    return toc - tic\n",
    "\n",
    "two_loop_time = time_function(classifier.compute_distances_two_loops, X_test)\n",
    "print('Two loop version took %f seconds' % two_loop_time)\n",
    "\n",
    "one_loop_time = time_function(classifier.compute_distances_one_loop, X_test)\n",
    "print('One loop version took %f seconds' % one_loop_time)\n",
    "\n",
    "no_loop_time = time_function(classifier.compute_distances_no_loops, X_test)\n",
    "print('No loop version took %f seconds' % no_loop_time)\n",
    "\n",
    "# You should see significantly faster performance with the fully vectorized implementation!\n",
    "# 使用完全矢量化的实现，您应该会看到明显更快的性能！\n",
    "# NOTE: depending on what machine you're using, \n",
    "# you might not see a speedup when you go from two loops to one loop, \n",
    "# and might even see a slow-down.\n",
    "# 根据所用的计算机，当您从两个循环转到一个循环时，您可能看不到加速，甚至可能会发现速度变慢。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross-validation\n",
    "\n",
    "We have implemented the k-Nearest Neighbor classifier but we set the value k = 5 arbitrarily. \n",
    "\n",
    "We will now determine the best value of this hyperparameter with cross-validation.\n",
    "\n",
    "我们已经实现了k最近邻居分类器，但是我们可以任意设置值k = 5。 \n",
    "\n",
    "现在，我们将通过交叉验证确定此超参数的最佳值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": [
     "code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k = 1, accuracy = 0.263000\n",
      "k = 1, accuracy = 0.257000\n",
      "k = 1, accuracy = 0.264000\n",
      "k = 1, accuracy = 0.278000\n",
      "k = 1, accuracy = 0.266000\n",
      "k = 3, accuracy = 0.239000\n",
      "k = 3, accuracy = 0.249000\n",
      "k = 3, accuracy = 0.240000\n",
      "k = 3, accuracy = 0.266000\n",
      "k = 3, accuracy = 0.254000\n",
      "k = 5, accuracy = 0.248000\n",
      "k = 5, accuracy = 0.266000\n",
      "k = 5, accuracy = 0.280000\n",
      "k = 5, accuracy = 0.292000\n",
      "k = 5, accuracy = 0.280000\n",
      "k = 8, accuracy = 0.262000\n",
      "k = 8, accuracy = 0.282000\n",
      "k = 8, accuracy = 0.273000\n",
      "k = 8, accuracy = 0.290000\n",
      "k = 8, accuracy = 0.273000\n",
      "k = 10, accuracy = 0.265000\n",
      "k = 10, accuracy = 0.296000\n",
      "k = 10, accuracy = 0.276000\n",
      "k = 10, accuracy = 0.284000\n",
      "k = 10, accuracy = 0.280000\n",
      "k = 12, accuracy = 0.260000\n",
      "k = 12, accuracy = 0.295000\n",
      "k = 12, accuracy = 0.279000\n",
      "k = 12, accuracy = 0.283000\n",
      "k = 12, accuracy = 0.280000\n",
      "k = 15, accuracy = 0.252000\n",
      "k = 15, accuracy = 0.289000\n",
      "k = 15, accuracy = 0.278000\n",
      "k = 15, accuracy = 0.282000\n",
      "k = 15, accuracy = 0.274000\n",
      "k = 20, accuracy = 0.270000\n",
      "k = 20, accuracy = 0.279000\n",
      "k = 20, accuracy = 0.279000\n",
      "k = 20, accuracy = 0.282000\n",
      "k = 20, accuracy = 0.285000\n",
      "k = 50, accuracy = 0.271000\n",
      "k = 50, accuracy = 0.288000\n",
      "k = 50, accuracy = 0.278000\n",
      "k = 50, accuracy = 0.269000\n",
      "k = 50, accuracy = 0.266000\n",
      "k = 100, accuracy = 0.256000\n",
      "k = 100, accuracy = 0.270000\n",
      "k = 100, accuracy = 0.263000\n",
      "k = 100, accuracy = 0.256000\n",
      "k = 100, accuracy = 0.263000\n"
     ]
    }
   ],
   "source": [
    "num_folds = 5\n",
    "k_choices = [1, 3, 5, 8, 10, 12, 15, 20, 50, 100]\n",
    "\n",
    "X_train_folds = []\n",
    "y_train_folds = []\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Split up the training data into folds. After splitting, X_train_folds and    #\n",
    "# y_train_folds should each be lists of length num_folds, where                #\n",
    "# y_train_folds[i] is the label vector for the points in X_train_folds[i].     #\n",
    "# Hint: Look up the numpy array_split function.                                #\n",
    "################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "# 将训练数据分成多个部分。\n",
    "# 拆分后，X_train_folds和y_train_folds都应为长度为num_folds的列表，\n",
    "# 其中y_train_folds [i]是X_train_folds [i]中各点的标签矢量。\n",
    "# 提示：查找numpy array_split函数。\n",
    "X_train_folds=np.array_split(X_train,num_folds)\n",
    "y_train_folds=np.array_split(y_train,num_folds)\n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "# A dictionary holding the accuracies for different values of k that we find\n",
    "# when running cross-validation. After running cross-validation,\n",
    "# k_to_accuracies[k] should be a list of length num_folds giving the different\n",
    "# accuracy values that we found when using that value of k.\n",
    "# 一个字典，包含我们在进行交叉验证时发现的k的不同值的精度。\n",
    "# 运行交叉验证后，k_to_accuracies [k]应该是长度num_folds的列表，\n",
    "# 其中给出了使用k的值时发现的不同精度值。\n",
    "k_to_accuracies = {}\n",
    "\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Perform k-fold cross validation to find the best value of k. For each        #\n",
    "# possible value of k, run the k-nearest-neighbor algorithm num_folds times,   #\n",
    "# where in each case you use all but one of the folds as training data and the #\n",
    "# last fold as a validation set. Store the accuracies for all fold and all     #\n",
    "# values of k in the k_to_accuracies dictionary.                               #\n",
    "################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "# 执行 k-fold 交叉验证，以找到k的最佳值。\n",
    "# 对于k的每个可能值，运行k_nearest-neighbor算法num_folds次，\n",
    "# 在每种情况下，您将其中一个fold以外的所有折叠用作训练数据，将最后一个fold用作验证集。\n",
    "# 将所有fold和所有k值的精度存储在k_to_accuracies字典中。\n",
    "pass\n",
    "\n",
    "# '''\n",
    "for k_val in k_choices:\n",
    "#   print 'k = '+str(k_val)\n",
    "  k_to_accuracies[k_val]=[]\n",
    "  for i in range(num_folds):\n",
    "    # print 'Cross-validation :'+ str(i)\n",
    "    X_train_cycle = np.concatenate([f for j,f in enumerate(X_train_folds) if j!=i ])\n",
    "    y_train_cycle = np.concatenate([f for j,f in enumerate(y_train_folds) if j!=i ])\n",
    "    X_val_cycle = X_train_folds[i]\n",
    "    y_val_cycle = y_train_folds[i]\n",
    "    #initialize the KNN\n",
    "    knn = KNearestNeighbor()\n",
    "    knn.train(X_train_cycle,y_train_cycle)\n",
    "    #calculate the accuracy\n",
    "    y_val_pred = knn.predict(X_val_cycle,k_val, 0)\n",
    "    num_correct = np.sum(y_val_cycle == y_val_pred)\n",
    "    k_to_accuracies[k_val].append(float(num_correct) / float(len(y_val_cycle)))\n",
    "# '''\n",
    "\n",
    "'''有bug\n",
    "for ki in k_choices:\n",
    "    for fi in range(num_folds):\n",
    "        #prepare the data\n",
    "        valindex=fi\n",
    "        X_traini = np.vstack((X_train_folds[0:fi]+X_train_folds[fi+1:num_folds]))\n",
    "        y_traini = np.hstack((y_train_folds[0:fi]+ y_train_folds[fi+1:num_folds]))\n",
    "        X_vali = np.array(X_train_folds[valindex])\n",
    "        y_vali = np.array(y_train_folds[valindex])\n",
    "        num_val=len(y_vali)\n",
    "        #initialize the KNN\n",
    "        classifier = KNearestNeighbor()\n",
    "        classifier.train(X_traini,y_traini)\n",
    "        #calculate the accuracy\n",
    "        dists = classifier.compute_distances_one_loop(X_vali)\n",
    "        y_val_pred = classifier.predict_labels(dists, k=ki)\n",
    "        num_correct = np.sum(y_val_pred == y_vali)\n",
    "        accuracy = float(num_correct) / num_val\n",
    "        print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))\n",
    "        k_to_accuracies[ki].append(accuracy)\n",
    "'''    \n",
    "\n",
    "'''有bug\n",
    "for k_ in k_choices:\n",
    "    k_to_accuracies.setdefault(k_, [])\n",
    "for i in range(num_folds):\n",
    "    classifier = KNearestNeighbor()\n",
    "    X_val_train = np.vstack(X_train_folds[0:i] + X_train_folds[i+1:])\n",
    "    y_val_train = np.vstack(y_train_folds[0:i] + y_train_folds[i+1:])\n",
    "    y_val_train = y_val_train[:,0]\n",
    "    classifier.train(X_val_train, y_val_train)\n",
    "    for k_ in k_choices:\n",
    "        y_val_pred = classifier.predict(X_train_folds[i], k=k_)\n",
    "        num_correct = np.sum(y_val_pred == y_train_folds[i][:,0])\n",
    "        accuracy = float(num_correct) / len(y_val_pred)\n",
    "        k_to_accuracies[k_] = k_to_accuracies[k_] + [accuracy]\n",
    "'''   \n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "# Print out the computed accuracies\n",
    "for k in sorted(k_to_accuracies):\n",
    "    for accuracy in k_to_accuracies[k]:\n",
    "        print('k = %d, accuracy = %f' % (k, accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the raw observations\n",
    "for k in k_choices:\n",
    "    accuracies = k_to_accuracies[k]\n",
    "    plt.scatter([k] * len(accuracies), accuracies)\n",
    "\n",
    "# plot the trend line with error bars that correspond to standard deviation\n",
    "# 用与标准偏差相对应的误差线绘制趋势线\n",
    "accuracies_mean = np.array([np.mean(v) for k,v in sorted(k_to_accuracies.items())])\n",
    "accuracies_std = np.array([np.std(v) for k,v in sorted(k_to_accuracies.items())])\n",
    "plt.errorbar(k_choices, accuracies_mean, yerr=accuracies_std)\n",
    "plt.title('Cross-validation on k')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Cross-validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 137 / 500 correct => accuracy: 0.274000\n"
     ]
    }
   ],
   "source": [
    "# Based on the cross-validation results above, choose the best value for k,   \n",
    "# retrain the classifier using all the training data, and test it on the test\n",
    "# data. You should be able to get above 28% accuracy on the test data.\n",
    "# 根据上面的交叉验证结果，为k选择最佳值，\n",
    "# 使用所有训练数据重新训练分类器，然后在测试数据上对其进行测试。\n",
    "# 您应该能够在测试数据上获得28％以上的准确性。\n",
    "best_k = 1\n",
    "\n",
    "classifier = KNearestNeighbor()\n",
    "classifier.train(X_train, y_train)\n",
    "y_test_pred = classifier.predict(X_test, k=best_k)\n",
    "\n",
    "# Compute and display the accuracy\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline Question 3**\n",
    "\n",
    "Which of the following statements about $k$-Nearest Neighbor ($k$-NN) are true in a classification setting, and for all $k$? Select all that apply.\n",
    "\n",
    "以下是关于k最近邻（k-NN）的以下哪个陈述在分类设置中正确，并且对于所有k？ 选择所有符合条件的。\n",
    "\n",
    "1. The decision boundary of the k-NN classifier is linear.\n",
    "\n",
    "k-NN分类器的决策边界是线性的。\n",
    "\n",
    "2. The training error of a 1-NN will always be lower than that of 5-NN.\n",
    "\n",
    "1-NN的训练误差将始终低于5-NN。\n",
    "\n",
    "3. The test error of a 1-NN will always be lower than that of a 5-NN.\n",
    "\n",
    "1-NN的测试误差将始终低于5-NN。\n",
    "\n",
    "4. The time needed to classify a test example with the k-NN classifier grows with the size of the training set.\n",
    "\n",
    "使用k-NN分类器对测试示例进行分类所需的时间随训练集的大小而增加。\n",
    "\n",
    "5. None of the above.\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$\n",
    "\n",
    "\n",
    "$\\color{blue}{\\textit Your Explanation:}$\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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